A federated learning-enabled predictive analysis to forecast stock market trends

Ardakani, Saeid Pourroostaei; Du, Nanjiang; Cai, Lin; Yang, Jiun-Chi; Bi, Zhuoran; Chen, Lejun

doi:10.1007/s12652-023-04570-4

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…They assert that ML models proved advantageous in the domains of investment decision-making and stock market analysis. Ardakani et al [26] presented a federated learning framework utilizing Random Forest, Support Vector Machine, and Linear Regression models for stock market forecast. To determine the optimal strategy, they contrasted federated learning with centralized and decentralized frameworks, and the strategies of learning frameworks for stock market prediction were elucidated by their results.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

Yao,

Zeng,

Zhu

et al. 2024

IJACSA

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The non-stationary, non-linear, and extremely noisy nature of stock price time series data, which are created from economic factors and systematic and unsystematic risks, makes it difficult to make reliable predictions of stock prices in the securities market. Conventional methods may improve forecasting accuracy, but they can additionally complicate the computations involved, increasing the likelihood of prediction errors. To address these issues, a novel hybrid model that combines recurrent neural networks and grey wolf optimization was introduced in the current study. The suggested model outperformed other models in the study with high efficacy, minimal error, and peak performance. Utilizing data from Alphabet stock spanning from June 29, 2023, to January 1, 2015, the effectiveness of the hybrid model was assessed. The gathered information comprised daily prices and trading volume. The outcomes showed that the suggested model is a reliable and effective method for analyzing and forecasting the time series of the financial market. The suggested model is additionally particularly well-suited to the volatile stock market and outperforms other recent strategies in terms of forecasting accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

Yao,

Zeng,

Zhu

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…Machine learning models were beneficial for stock market analysis and investment decisionmaking, according to Tsai et al [18]. Ardakani et al [19] proposed a federated learning framework for stock market prediction using Random Forest, Support Vector Machine, and Linear Regression models. They compared federated learning to centralized and decentralized frameworks to find the best approach.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Federated learning had a lower model training delay than benchmarks for Linear Regression (9.7 s) and Random Forest (515 s), while decentralized learning saves time for Support Vector Machine (3847 s). Their findings illuminated stock market prediction learning framework strategies [19]. A novel stock price prediction method by Mamluatul et al [20] uses machine learning, stock price data, technical indicators, and Google trends.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Financial Markets Utilizing an Innovatively Optimized Hybrid Model: A Case Study of the Hang Seng Index

YANG

2024

IJACSA

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Stock trading is a highly consequential and frequently discussed subject in the realm of financial markets. Due to the volatile and unpredictable nature of stock prices, investors are perpetually seeking methods to forecast future trends in order to minimize losses and maximize profits. Nevertheless, despite the ongoing investigation of various approaches to optimize the predictive efficacy of models, it is indisputable that a method for accurately forecasting forthcoming market trends does not yet exist. A multitude of algorithms are currently being employed to forecast stock prices due to significant developments that have occurred in recent years. An innovative algorithm for predicting stock prices are examined in this paper which is a Gated Recurrent Unit combined with the Aquila optimizer. A comprehensive data implementation utilizing the Hang Seng Index stock price was executed as a dataset of this research which was collected between the years of 2015 and the end of June 2023. In the study, several additional methods for predicting stock market movements are also detailed. A comprehensive comparative analysis of the stock price prediction performances of the aforementioned algorithms has also been carried out to offer a more in-depth analysis and then the results are displayed in an understandable tabular and graphical manner. The proposed model obtained the values of 0.9934, 0.71, 143.62, and 36530.58, for 𝑹 𝟐 , MAPE, MAE, and MSE, respectively. These results proved the efficiency and accuracy of the suggested method and it was determined that the proposed model algorithm produces results with a high degree of accuracy and performs the best when it comes to forecasting a time series or stock price.

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“…This conference paper explores the integration of Long Short-Term Memory (LSTM) and Random Forest algorithms for stock price prediction, showing promising results [5]. In "A federated learningenabled predictive analysis to forecast stock…" [6], The authors suggested machine learning models, such as MLP, to assist investors in making buy-or-sale decisions by fusing big data approaches with fundamental analysis. Cakra practiced the Random Forest and Naive Bayes algorithms to categorize tweets for sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

Predicting Twitter stock price using linear regression, random forest, and MLP regression

Huang

2024

TNS

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Since the chaos and complexity in the stock market, predicting stock prices with machinery approaches helps assert a new perspective for people to reach satisfactory results in stock analysis. The stock price of Twitter reflects its market value and performance, which are influenced by various factors such as user engagement, revenue, news, and sentiment. Predicting the stock price of Twitter is a challenging task that requires sophisticated methods and data analysis. In this essay, the author compares three different machine learning models to predict Twitters stock price: linear regression, random forest, and MLP regression. The paper uses historical data on Twitters stock price and various features such as volume, peak value, and trough value. The researcher evaluates the performance of each model using metrics such as Mean-squared Error and R-squared and finds that MLP regression outperforms the accuracy and generalization of the other two models. The author also discusses the limitations and implications of our findings for investors and researchers.

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A federated learning-enabled predictive analysis to forecast stock market trends

Cited by 13 publications

References 23 publications

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

A Novel Proposal for Improving Economic Decision-Making Through Stock Price Index Forecasting

Prediction of Financial Markets Utilizing an Innovatively Optimized Hybrid Model: A Case Study of the Hang Seng Index

Predicting Twitter stock price using linear regression, random forest, and MLP regression

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